Metrics for the comparative analysis of geospatial datasets with applications to high-resolution grid-based population data
Geospatial data sciences have emerged as critical requirements for high-priority application solutions in diverse areas, including, but not limited to, the mitigation of natural and man-made disasters. Three sets of metrics, adopted or customized from geo-statistics, applied meteorology and signal processing, are tested in terms of their ability to evaluate geospatial datasets, specifically two population databases commonly used for disaster preparedness and consequence management. The two high-resolution, grid-based population datasets are the following: The LandScan dataset available from the Geographic Information Science and Technology (GIST) group at the Oak Ridge National Laboratory (ORNL), and the Gridded Population of the World (GPW) dataset available from the Center for International Earth Science Information Network (CIESIN) group at Columbia University. Case studies evaluate population data across the globe, specifically, the metropolitan areas of Washington DC, USA, Los-Angeles, USA, and Houston, USA, and London, UK, as well as the country of Iran. The geospatial metrics confirm that the two population datasets have significant differences, especially in the context of their utility for disaster readiness and mitigation. While this paper primarily focuses on grid based population datasets and disaster management applications, the sets of metrics developed here can be generalized to other geospatial datasets and applications. Future research needs to develop metrics for geospatial and temporal risks and associated uncertainties in the context of disaster management.
KeywordsGeospatial data Population Statistical evaluation Disaster management
- Bhaduri, B., Bright, E., Coleman, P., & Dobson, J. (2002). LandScan: Locating people is what matters. Geoinformatics, 5(2), 34–37.Google Scholar
- Box, G., Jenkins, G. M., & Reinsel, G. (1994). Time series analysis: Forecasting and control (3rd ed.). Prentice Hall.Google Scholar
- Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT), 2004. Gridded Population of the World (GPW), Version 3. Palisades, NY: Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw/global.jsp (Verified June 2007).
- Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling spatial uncertainty. Wiley.Google Scholar
- Cressie, N. (1993). Statistics for spatial data. Wiley.Google Scholar
- Deichmann, U. (1996). A Review of spatial population database design and modelling. National Center for Geographic Information and Analysis (NCGIA). Santa Barbara, CA, USA: University of California, Santa Barbara (UCSB).Google Scholar
- Deichmann, U., Balk, D., & Yetman, G. (2001). Transforming population data for interdisciplinary usages: From census to grid. Unpublished manuscript available on-line at: http://sedac.ciesin.columbia.edu/plue/gpw/GPWdocumentation.pdf (Verified: June 2007).
- Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L., Arnold, M., Agwe, J., Buys, P., Kjekstad, O., Lyon, B., & Yetman, G. (2005). Natural disaster hotspots: A Global risk analysis, The World Bank, 132 pp.Google Scholar
- Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., & Worley, B. A. (2000). LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 66(7), 849–857.Google Scholar
- Draper, N. R., & Smith, H. (1998). Applied regression analysis. Wiley.Google Scholar
- Fotheringham, A. S., Brundson, C., & Charlton, M. (2000). Quantitative geography, perspectives on spatial data analysis. Sage.Google Scholar
- Ganguly, R. A., Protopopescu, V., & Sorokine, A. (2005). A bottom-up strategy for uncertainty quantification in complex geo-computational models. Proceedings of the GeoComputation 2005 Conference. Michigan: Ann Arbor.Google Scholar
- Garb, J. L., Cromley, R. G., & Wait, R. B. (2007). Estimating populations at risk for disaster preparedness and response. Journal of Homeland Security and Emergency Management, 4(1), Article 3.Google Scholar
- Mills, T. C. (1991). Time series techniques for economists. Cambridge University Press, 387 pp.Google Scholar
- NRC [National Research Council] (2007). Tools and methods for estimating populations at risk from natural disasters and complex humanitarian crises. The National Academies Press, 248 pp.Google Scholar
- O’Sullivan, D., & Unwin, D. (2003). Geographic information analysis. John Wiley.Google Scholar
- Ripley. B. D. (2004). Spatial statistics. John Wiley and Sons.Google Scholar
- Sabesan, A., Abercrombie, K. A., & Ganguly, A. R. (2006). Uncertainty estimates in population distribution models, Oak Ridge National Laboratory Technical Report 2006/540. U.S. Department of Energy.Google Scholar
- Stanski, H. R., Wilson, L. J., & Burrows, W. R. (1989). Survey of common verification methods in meteorology. World Weather Watch Tech. Rep. 8, WMO Tech. Doc. 358, World Meteorological Organization, 114 pp.Google Scholar